Machine Learning textbook

Machine Learning, Tom Mitchell, McGraw Hill, 1997.

Machine Learning is the study of computer algorithms that improve automatically through experience. Applications range from datamining programs that discover general rules in large data sets, to information filtering systems that automatically learn users' interests.
This book provides a single source introduction to the field. It is written for advanced undergraduate and graduate students, and for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed.

Chapter Outline: (or see the detailed table of contents (postscript))

1. Introduction

2. Concept Learning and the General-to-Specific Ordering

3. Decision Tree Learning

4. Artificial Neural Networks

5. Evaluating Hypotheses

6. Bayesian Learning

7. Computational Learning Theory

8. Instance-Based Learning

9. Genetic Algorithms

10. Learning Sets of Rules

11. Analytical Learning

12. Combining Inductive and Analytical Learning

13. Reinforcement Learning

414 pages. ISBN 0070428077
New book chapters available for download.

Reviews of this book.

Ordering information.

Lecture slides for instructors, in both postscript and latex source

Software and data discussed in the text.

Errata for printings one and two ( postscript )( pdf )

About the author.